Deploying LLM applications at scale requires reliable cloud native infrastructure. This talk explores how to run production RAG pipelines using Kubernetes, covering vector search services, scalable inference with vLLM, and distributed embedding pipelines. We will discuss observability, cost optimization, and autoscaling strategies for real world AI workloads running in containerized environments.